• 한국경영과학회지
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• 제38권1호
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• pp.45-59
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• 2013

A long-term forecasting method for a new product in early stage of diffusion is proposed. The method includes a constrained non-linear least square estimation with the logistic diffusion model. The constraints would be critical market informations such as market potential, peak point, and take-off. Findings on 20 cases having almost full life cycle are that (i) combining any market information improves the forecasting accuracy, (ii) market potential is the most stable information, and (iii) peak point and take-off information have negative effect in case of overestimation.

• 노동경제논집
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• 제37권1호
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• pp.87-116
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• 2014

노동시장 위기관리 시스템의 일환으로 국내외 경제상황 변동으로 야기되는 고용변화를 사전에 감지하는 단기고용변동의 상시적 예측이 요구된다. 이를 위해 본 논문은 경기선행지수 작성방식을 준용하여 비농림 민간부문 임금근로자 변동을 단기적으로 예측하는 고용선행지수를 개발하였다. 고용선행지수는 고용수준 그 자체보다 고용 동향의 국면 및 전환 시점, 변동 속도등 고용의 변화 방향을 조기 탐지하는 것에 중점을 두어 작성되었다. 개발된 지수에 대해 국면 전환 선행성 평가와 고용수준 변동 예측에 대한 모의실험을 통해 검증하고 활용방안을 제시한다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2000년도 추계정기학술대회:지능형기술과 CRM
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• pp.489-497
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• 2000

After IMF situation, the money market environment is changing rapidly. Therefore, many companies including financial institutions and many individual investors are concerned about forecasting the money market, and they make an effort to insure the various profit and hedge methods using derivatives like option, futures and swap. In this research, we developed a prototype of forecasting system for KOSPI 200 option, especially call option, trading using artificial neural networks(ANN), To avoid the overfitting problem and the problem involved int the choice of ANN structure and parameters, we employed the ANN ensemble approach. We conducted two types of simulation. One is conducted with the hold signals taken into account, and the other is conducted without hold signals. Even though our models show low accuracy for the sample set extracted from the data collected in the early stage of IMF situation, they perform better in terms of profit and stability than the model that uses only the theoretical price.

• 한국건설관리학회논문집
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• 제10권1호
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• pp.102-113
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• 2009

공동주택 개발사업의 기획 추진시 사업의 성공적인 성과를 이루기 위해서는 기획단계에서의 수익성과 분양성에 대한 현실적인 평가와 분석을 통한 정량적 예측과 이에 대한 보완 개선적인 과정이 가장 중요한 요소라 할 수 있다. 특히, 개발사업 수행 중에서 기획단계에서의 초기 분양율 예측을 통한 사업타당성 분석 및 검증은 전체사업의 기대수익과 성공가능여부 예측면에서 중점 고려요소들을 분석하고 미흡한 요소에 대한 투자와 개선을 유도함으로써 개발사업의 리스크 인자를 감소시키고 사업의 성공가능성을 확대시킴으로써 그 효과를 극대화 할 수 있다. 이와 같은 관점에서 본 연구에서는 공동주택 개발사업 타당성 검토요소 및 항목간 중요도 산정지표 연구를 기반으로 하여, 민간 공동주택 사례별 실제 초기분양율을 비교 분석함으로써, 산정지표 중 누락된 항목 및 추가적으로 고려해야 할 항목을 중요도 조사결과를 바탕으로 추가적으로 배점화하여 4분야 9항목의 33세부 평가내용으로 조합하여 공동주택의 수익성과 분양성 예측을 위한 효율적인 평가항목과 기준을 제시하였다.

• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2009년도 정보통신설비 학술대회
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• pp.157-161
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• 2009

In recent years, there is a remarkable progress in ICTs (Information and Communication Technologies), and then many attempts to apply ICTs to other industries are being made. In the field of disaster managements, ICTs such as RFID (Radio Frequency IDentification) and USN (Ubiquitous Sensor Network) are used to provide safe environments. Actually, various types of early warning systems using USN are now widely used to monitor natural disasters such as floods, landslides and earthquakes, and also to detect human-caused disasters such as fires, explosions and collapses. These early warning systems issue alarms rapidly when a disaster is detected or an event exceeds prescribed thresholds, and furthermore deliver alarm messages to disaster managers and citizens. In general, these systems consist of a number of various sensors and measure real-time stream data, which requires an efficient and rapid data processing technique. In this study, an event-driven architecture (EDA) is presented to collect event effectively and to provide an alert rapidly. A publish/subscribe event processing method to process simple event is introduced. Additionally, a complex event processing (CEP) technique is introduced to process complex data from various sensors and to provide prompt and reasonable decision supports when many disasters happen simultaneously. A basic concept of CEP technique is presented and the advantages of the technique in disaster management are also discussed. Then, how the main processing methods of CEP such as aggregation, correlation, and filtering can be applied to disaster management is considered. Finally, an example of flood forecasting and early alarm system in which CEP is incorporated is presented It is found that the CEP based on the EDA will provide an efficient early warning method when disaster happens.

• 한국물환경학회지
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• 제27권4호
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• pp.533-541
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• 2011

Algal blooms have caused problems for drinking water as well as eutrophication. However it is difficult to control algal blooms by current warning manual in rainy season because the algal blooms happen in a few days. The water quality data, which have high correlations with Chlorophyll-a on Daecheongho station, were analyzed and chosen as input data of Artificial Neural Networks (ANN) for training pattern changes. ANN was applied to early forecasting of algal blooms, and ANN was assessed by forecasting errors. Water temperature, pH and Dissolved oxygen were important factors in the cross correlation analysis. Some water quality items like Total phosphorus and Total nitrogen showed similar pattern to the Chlorophyll-a changes with time lag. ANN model (No. 3), which was calibrated by water temperature, pH and DO data, showed lowest error. The combination of 1 day, 3 days, 7 days forecasting makes outputs more stable. When automatic monitoring data were used for algal bloom forecasting in Daecheong reservoir, ANN model must be trained by just input data which have high correlation with Chlorophyll-a concentration. Modular type model, which is combined with the output of each model, can be effectively used for stable forecasting.

• 한국지리정보학회지
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• 제10권2호
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• pp.91-102
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• 2007

대청호와 같이 규모가 큰 인공호수에서의 유해조류 발생을 사전에 예측하고 대응하기 위해서는 생물 화학적 연구와 더불어 GIS, RS 기술을 활용하는 지역분석 전산시스템의 개발도 필요하다. 이 논문의 목표는 대청호에서의 유해조류 생산을 저감시키기 위하여 남조류의 발생에 대한 예보모델을 개발하고 GIS를 기반으로 한 남조류 발생 조기예보시스템을 개발하는 것이다. 이를 위해 대청호에서의 남조류 발생과 환경인자와의 관계에 대한 선행연구 사례들을 분석하였으며, 그 결과 남조류 예보모델 개발을 위해 사용할 환경인자로서 강수와 기온을 선정하였다. 선정한 환경인자와 남조류 발생과의 정성적 상관관계 분석결과를 토대로 대청호의 남조류발생을 수역별로 예측할 수 있는 Rump 모델을 개발하였는데, 이 예보모델은 남조류의 최초발생시기와 급성장시기에 대한 예측이 가능하다. 개발된 예보모델은 GIS를 기반으로 한 남조류 대발생 조기예보시스템에 적용하였으며, 그 결과 대청호에서의 남조류 대발생을 예측하고 관련 자료들을 관리할 수 있는 지리정보시스템이 개발되었다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.134-134
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• 2022

Streamflow forecasting plays a crucial role in water resource control, especially in highly urbanized areas that are very vulnerable to flooding during heavy rainfall event. In addition to providing the accurate prediction, the evaluation of effects and importance of the input predictors can contribute to water manager. Recently, machine learning techniques have applied their advantages for modeling complex and nonlinear hydrological processes. However, the techniques have not considered properly the importance and uncertainty of the predictor variables. To address these concerns, we applied the GA-BART, that integrates a genetic algorithm (GA) with the Bayesian additive regression tree (BART) model for hourly streamflow forecasting and analyzing input predictors. The Jungrang urban basin was selected as a case study and a database was established based on 39 heavy rainfall events during 2003 and 2020 from the rain gauges and monitoring stations. For the goal of this study, we used a combination of inputs that included the areal rainfall of the subbasins at current time step and previous time steps and water level and streamflow of the stations at time step for multistep-ahead streamflow predictions. An analysis of multiple datasets including different input predictors was performed to define the optimal set for streamflow forecasting. In addition, the GA-BART model could reasonably determine the relative importance of the input variables. The assessment might help water resource managers improve the accuracy of forecasts and early flood warnings in the basin.

• 기술혁신학회지
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• 제14권spc호
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• pp.1232-1255
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• 2011

본 연구에서는 신제품 확산 모델 활용에 있어서 보다 적은 노력이 필요하지만 객관적이고 신속한 활용을 가능하게 만들어줄 모형을 제안한다. 기대주기 모델과 소비자 수용 모델이라는 이론적 배경을 바탕으로, 서지분석학과 초기 시장의 규모만으로 최대 잠재 시장을 추정해냄으로써 대표적인 확산 모형인 배스 모형(Bass model)에 필요한 주요 모수를 제공하는 방법을 제시했다. 모형의 예측력을 하이브리드자동차 사례를 통해 분석한 결과, 모형의 예측결과는 여러 가지 객관적인 정보를 통해 추정한 잠재 시장과 유사한 규모를 성공적으로 예측해 내어 모형의 활용 가능성을 확인할 수 있었다. 제안된 모형이 제공한 최대 잠재 시장은 다른 성장곡선모형에도 바로 적용 가능하다는 점을 볼 때 제안된 모형은 서지분석학을 통한 기술 확산 예측과 유망기술 탐색에 새로운 방향을 제시했다고 할 것이다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.141-141
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• 2022

Drought is a global phenomenon that affects almost all landscapes and causes major damages. Due to non-linear nature of contributing factors, drought occurrence and its severity is characterized as stochastic in nature. Early warning of impending drought can aid in the development of drought mitigation strategies and measures. Thus, drought forecasting is crucial in the planning and management of water resource systems. The primary objective of this study is to make improvement is existing drought forecasting techniques. Therefore, we proposed an improved version of Seasonal Autoregressive Integrated Moving Average (SARIMA) model (MD-SARIMA) for reliable drought forecasting with three years lead time. In this study, we selected four watersheds of Han River basin in South Korea to validate the performance of MD-SARIMA model. The meteorological data from 8 rain gauge stations were collected for the period 1973-2016 and converted into watershed scale using Thiessen's polygon method. The Standardized Precipitation Index (SPI) was employed to represent the meteorological drought at seasonal (3-month) time scale. The performance of MD-SARIMA model was compared with existing models such as Seasonal Naive Bayes (SNB) model, Exponential Smoothing (ES) model, Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal components (TBATS) model, and SARIMA model. The results showed that all the models were able to forecast drought, but the performance of MD-SARIMA was robust then other statistical models with Wilmott Index (WI) = 0.86, Mean Absolute Error (MAE) = 0.66, and Root mean square error (RMSE) = 0.80 for 36 months lead time forecast. The outcomes of this study indicated that the MD-SARIMA model can be utilized for drought forecasting.